home-ops: Skill for Claude Code

.claude/skills/intel-gpu/SKILL.md

intel-gpu is a skill for Claude Code from Aviator-Coding/home-ops. It costs 93 tokens per session (933 once invoked), scanned A, original, MIT.

A project guide for using Intel GPUs in a Kubernetes cluster, including device plugins, video processing, inference, and GPU monitoring. It documents device names and access rules that workloads depend on.

In plain words
What is it for?
Use it when changing Intel GPU device exposure, VA-API video processing, GPU inference workloads, or Grafana GPU metrics and alerts.
Why use it?
It prevents changes from making video transcoding fail silently or locking workloads out of the correct GPU devices. It also explains why video processing and AI inference may react differently to the same device change.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions AGENTS.md.

This is Aviator-Coding/home-ops's own configuration. It tells Claude Code how to work on home-ops itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything home-ops configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Aviator-Coding/home-ops. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Aviator-Coding/home-ops/main/.claude/skills/intel-gpu/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Aviator-Coding/home-ops

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for intel-gpu

README.md
[![agentmods](https://agentmods.dev/badge/skills/aviator-coding/home-ops/intel-gpu.svg)](https://agentmods.dev/skills/aviator-coding/home-ops/intel-gpu)
Your own site
<a href="https://agentmods.dev/skills/aviator-coding/home-ops/intel-gpu"><img src="https://agentmods.dev/badge/skills/aviator-coding/home-ops/intel-gpu.svg" alt="Measured on agentmods" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 933 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00093 $0.00933
Opus 5 $0.00046 $0.00466
Sonnet 5 $0.00019 $0.00187
Haiku 4.5 $0.00009 $0.00093

Measured 3d ago against content hash f648e0ffc325, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

intel-gpu scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/intel-gpu/SKILL.md · 16 lines

What it actually says

Intel GPUs: device plugins, VA-API and telemetry

Relocated verbatim from AGENTS.md on 2026-09-01 so it loads only when this subsystem is in play. The text below is unchanged; only line breaks were inserted. AGENTS.md keeps a one-sentence pointer. Add new findings here or to the owning document, not back into AGENTS.md - see its "Maintaining this file" section for the rule.

  • NEVER rename a DRM device node with generic-device-plugin's mountPath, and never ship a GPU change without running the VA-API check. The b70 group remaps the Arc B70 to card0/renderD128; that rename is fatal to VA-API, because libdrm ignores the path you pass, fstat()s the fd, reads /sys/dev/char/<major>:<minor>/uevent and reopens the canonical DEVNAME it finds there (dri/renderD129) - a path the container does not have. vaGetDisplayDRM() then fails before any driver loads. Level Zero (vllm, comfyui) opens whatever /dev/dri/render* exists and is unaffected, so the AI stack stays green while transcoding is completely dead; that asymmetry hid a total 3-day Tdarr outage (2026-08-26, PR #1443). VA-API consumers must use devic.es/b70-vaapi, which exposes the same card under its kernel names. Two related traps in the same config: its device IDs are sha1(count + every host path in the group), so adding a path to an existing group changes all its IDs and invalidates kubelet's live allocations for pods holding that resource (add a new group instead); and the config is a subPath mount, which kubelet never refreshes, so the configMapGenerator hash must stay enabled or a config-only edit is inert until someone restarts the DaemonSet by hand. Allocatable capacity is not proof that transcoding works - verification commands: docs/media-stack.md "Verifying VA-API after a GPU change"; mechanism and evidence: docs/ai-gpu-changelog.md (2026-08-29).

  • No xpu-smi/level-zero/DCGM-equivalent GPU exporter is deployed, so per-engine busy %, VRAM utilization, and clocks are not queryable in Prometheus for either Intel GPU. Verified live 2026-08-26 while building ai/gpu-node-dashboard. What does exist: the discrete Arc B70 (talos-3 only, devic.es/b70) surfaces a kernel xe-driver hwmon chip (node_hwmon_chip_names{chip_name="xe"}, chip id 0000:02:01_0_0000:03:00_0) with temp2=package/temp3=VRAM (node_hwmon_temp_celsius), power1 cap/crit (node_hwmon_power_cap_watt/_crit_watt, static config values, not live draw), fan1 RPM (node_hwmon_fan_rpm), and energy counters energy1=card/energy2=package (node_hwmon_energy_joule_total, rate() them for live watts - there is no direct power-draw gauge). The fleet iGPU (gpu.intel.com/xe, on-die, all 3 nodes) has no dedicated hwmon chip at all - only kube-state-metrics' device-plugin kube_node_status_allocatable/kube_pod_container_resource_requests{resource="gpu_intel_com_xe"} show allocation/usage, no thermal or utilization signal. Dashboard and verified panel list: kubernetes/apps/base/ai/gpu-node-dashboard/app/gpu-node.json.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 16 lines · 93 tokens per session scan A f648e0ffc325

Subscribe to this mod's changes

intel-gpu is a skill published in the GitHub repository Aviator-Coding/home-ops (2 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 933 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.

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